Bibliographic record
Abstract
n the preparation of a lengthy work such as this there are always many people and institutions who must be thanked.The Social Sciences and Humanities Research Council of Canada provided funding for the earliest aspects of this research project through a doctoral studies grant, and the University of North Dakota aided the completion of this work with both a developmental leave and several small grants.Dr. Ed Rea lived through several iterations of this project, first as a dissertation advisor and then as a mentor, a much valued critic, and finally as a dear friend.The aid and encouragement of the entire History Department at the University of Manitoba has also been of incredible value to me as I pursued this project, but the critical contributions of Gerry Friesen, John Kendle, and Mark Gabbert must be singled out for particular attention.Like most historians I also owe a profound debt of gratitude to archivists and librarians.The staff at the NAC were of great assistance throughout my research work, while those at the PAM went far beyond the call of duty to aid me in almost every aspect of this project-and several others over the years.Indeed, everyone at PAM made working in this archive a pure joy.The staff at the Legislative Library of Manitoba, the Elizabeth Dafoe Library-especially
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.628 | 0.422 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".